Trajectory-User Linking (TUL) task aims to accurately match anonymous trajectories to their corresponding users. As a critical task in mobility data mining, its resolution is crucial for a wide range of downstream applications, including personalized recommendations, urban planning, and public safety. However, existing methods primarily focus on point-level data while neglecting the holistic travel semantics embedded within urban road network. Moreover, they overlook the higher-order relationships among trajectories of different users. Consequently, we propose a Multiscale Semantics-Relationships Fusion Representation Model for TUL, namely MSRTUL. Specifically, we first propose a multiscale destination-oriented trajectory semantics encoder, which captures destination-oriented spatial-temporal semantics from both road-level and zone-level trajectories to encode the holistic travel semantics. Subsequently, we design a multiscale trajectory high-order relationships encoder, which jointly models higher-order relationships among trajectories, points, and categories through the hypergraph. To effectively combine the travel semantics and higher-order relationships, we design a dual-view fusion layer. We conduct extensive experiments on three real-world datasets, demonstrating that MSRTUL achieves significant improvements over multiple baselines in the TUL.